#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# The below software in this distribution may have been modified by THL A29 Limited ("Tencent Modifications").
# All Tencent Modifications are Copyright (C)
# THL A29 Limited.
#
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""PyTorch optimization for BERT model."""

import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
import amp_C
import torch.distributed as dist


multi_tensor_l2norm = amp_C.multi_tensor_l2norm
lamb_compute_update = amp_C.multi_tensor_lamb_stage1_cuda
lamb_apply_update = amp_C.multi_tensor_lamb_stage2_cuda
scale = amp_C.multi_tensor_scale


def warmup_cosine(x, warmup=0.002):
    # Function of warmup, cosine
    if x < warmup:
        return x / warmup
    return 0.5 * (1.0 + torch.cos(math.pi * x))


def warmup_constant(x, warmup=0.002):
    # Function of warmup, constant
    if x < warmup:
        return x / warmup
    return 1.0


def warmup_linear(x, warmup=0.002):
    # Function of warmup, linear
    if x < warmup:
        return x / warmup
    return max((x - 1.) / (warmup - 1.), 0.)


def warmup_poly(x, warmup=0.002, degree=0.5):
    # Function of warmup, poly
    if x < warmup:
        return x / warmup
    return (1.0 - x) ** degree


SCHEDULES = {
    'warmup_cosine': warmup_cosine,
    'warmup_constant': warmup_constant,
    'warmup_linear': warmup_linear,
    'warmup_poly': warmup_poly,
}


class BertAdam(Optimizer):
    """Implements BERT version of Adam algorithm with weight decay fix.
    Params:
        lr: learning rate
        warmup: portion of t_total for the warmup, -1  means no warmup. Default: -1
        t_total: total number of training steps for the learning
            rate schedule, -1  means constant learning rate. Default: -1
        schedule: schedule to use for the warmup (see above). Default: 'warmup_linear'
        b1: Adams b1. Default: 0.9
        b2: Adams b2. Default: 0.999
        e: Adams epsilon. Default: 1e-6
        weight_decay: Weight decay. Default: 0.01
        max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
    """

    def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
                 ba1=0.9, ba2=0.999, e=1e-6, weight_decay=0.01,
                 max_grad_norm=1.0):
        if lr is not required and lr < 0.0:
            raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
        if schedule not in SCHEDULES:
            raise ValueError("Invalid schedule parameter: {}".format(schedule))
        if not 0.0 <= warmup < 1.0 and not warmup == -1:
            raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
        if not 0.0 <= ba1 < 1.0:
            raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(ba1))
        if not 0.0 <= ba2 < 1.0:
            raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(ba2))
        if not e >= 0.0:
            raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
        defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
                        b1=ba1, b2=ba2, e=e, weight_decay=weight_decay,
                        max_grad_norm=max_grad_norm)
        super(BertAdam, self).__init__(params, defaults)

    def get_lr(self):
        lr = []
        for bert_adam_group in self.param_groups:
            for p in bert_adam_group['params']:
                state = self.state[p]
                if len(state) == 0:
                    return [0]
                if bert_adam_group['t_total'] != -1:
                    schedule_fct = SCHEDULES[bert_adam_group['schedule']]
                    lr_scheduled = bert_adam_group['lr'] * schedule_fct(state['step'] / bert_adam_group['t_total'],
                                                                        bert_adam_group['warmup'])
                else:
                    lr_scheduled = bert_adam_group['lr']
                lr.append(lr_scheduled)
        return lr

    def step(self, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()
        for bert_adam_group in self.param_groups:
            for p in bert_adam_group['params']:
                if p.dtype == torch.float16:
                    p.data = p.data.float()
                # if p.numel() == 1:
                # print('Catch amax',p)
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['next_m'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['next_v'] = torch.zeros_like(p.data)
                    state['eff_lr'] = 0

                next_m, next_v = state['next_m'], state['next_v']
                beta1, beta2 = bert_adam_group['b1'], bert_adam_group['b2']

                # Add grad clipping
                if bert_adam_group['max_grad_norm'] > 0:
                    clip_grad_norm_(p, bert_adam_group['max_grad_norm'])

                # Decay the first and second moment running average coefficient
                # In-place operations to update the averages at the same time
                next_m.mul_(beta1).add_(1 - beta1, grad)
                next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                update = next_m / (next_v.sqrt() + bert_adam_group['e'])

                # Just adding the square of the weights to the loss function is *not*
                # the correct way of using L2 regularization/weight decay with Adam,
                # since that will interact with the m and v parameters in strange ways.
                #
                # Instead we want to decay the weights in a manner that doesn't interact
                # with the m/v parameters. This is equivalent to adding the square
                # of the weights to the loss with plain (non-momentum) SGD.
                if bert_adam_group['weight_decay'] > 0.0:
                    update += bert_adam_group['weight_decay'] * p.data

                if bert_adam_group['t_total'] != -1:
                    schedule_fct = SCHEDULES[bert_adam_group['schedule']]
                    lr_scheduled = bert_adam_group['lr'] * schedule_fct(state['step'] / bert_adam_group['t_total'],
                                                                        bert_adam_group['warmup'])
                else:
                    lr_scheduled = bert_adam_group['lr']

                update_with_lr = lr_scheduled * update
                state['eff_lr'] = lr_scheduled
                p.data.add_(-update_with_lr)

                state['step'] += 1

                p.data = p.data.half()
                p.grad = None

        return loss


class LSQAdam(Optimizer):
    """Implements BERT version of Adam algorithm with weight decay fix.
    Params:
        lr: learning rate
        warmup: portion of t_total for the warmup, -1  means no warmup. Default: -1
        t_total: total number of training steps for the learning
            rate schedule, -1  means constant learning rate. Default: -1
        schedule: schedule to use for the warmup (see above). Default: 'warmup_linear'
        b1: Adams b1. Default: 0.9
        b2: Adams b2. Default: 0.999
        e: Adams epsilon. Default: 1e-6
        weight_decay: Weight decay. Default: 0.01
        max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
    """

    def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
                 b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01,
                 max_grad_norm=1.0):
        if lr is not required and lr < 0.0:
            raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
        if schedule not in SCHEDULES:
            raise ValueError("Invalid schedule parameter: {}".format(schedule))
        if not 0.0 <= warmup < 1.0 and not warmup == -1:
            raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
        if not 0.0 <= b1 < 1.0:
            raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
        if not 0.0 <= b2 < 1.0:
            raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
        if not e >= 0.0:
            raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
        defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
                        b1=b1, b2=b2, e=e, weight_decay=weight_decay,
                        max_grad_norm=max_grad_norm)
        super(LSQAdam, self).__init__(params, defaults)

    def get_lr(self):
        lr = []
        for lsq_adam_group in self.param_groups:
            for p in lsq_adam_group['params']:
                state = self.state[p]
                if len(state) == 0:
                    return [0]
                if lsq_adam_group['t_total'] != -1:
                    schedule_fct = SCHEDULES[lsq_adam_group['schedule']]
                    lr_scheduled = lsq_adam_group['lr'] * schedule_fct(state['step'] / lsq_adam_group['t_total'],
                                                                       lsq_adam_group['warmup'])
                else:
                    lr_scheduled = lsq_adam_group['lr']
                lr.append(lr_scheduled)
        return lr

    def step(self, all_reduce = True, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()
        for group in self.param_groups:
            for p in group['params']:
                dtype = p.dtype
                if p.dtype == torch.float16 or p.dtype == torch.bfloat16:
                    p.data = p.data.float()
                # if p.numel() == 1:
                # print('Catch amax',p)
                if p.grad is None:
                    continue
                if all_reduce:
                    dist.all_reduce(p.grad)
                    p.grad.mul_(1/dist.get_world_size())
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['next_m'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['next_v'] = torch.zeros_like(p.data)
                    state['eff_lr'] = 0

                next_m, next_v = state['next_m'], state['next_v']
                beta1, beta2 = group['b1'], group['b2']

                # Add grad clipping
                if group['max_grad_norm'] > 0:
                    clip_grad_norm_(p, group['max_grad_norm'])

                # Decay the first and second moment running average coefficient
                # In-place operations to update the averages at the same time
                next_m.mul_(beta1).add_(1 - beta1, grad)
                next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                update = next_m / (next_v.sqrt() + group['e'])

                # Just adding the square of the weights to the loss function is *not*
                # the correct way of using L2 regularization/weight decay with Adam,
                # since that will interact with the m and v parameters in strange ways.
                #
                # Instead we want to decay the weights in a manner that doesn't interact
                # with the m/v parameters. This is equivalent to adding the square
                # of the weights to the loss with plain (non-momentum) SGD.
                if group['weight_decay'] > 0.0:
                    update += group['weight_decay'] * p.data

                if group['t_total'] != -1:
                    schedule_fct = SCHEDULES[group['schedule']]
                    lr_scheduled = group['lr'] * schedule_fct(state['step'] / group['t_total'], group['warmup'])
                else:
                    lr_scheduled = group['lr']

                update_with_lr = lr_scheduled * update
                state['eff_lr'] = lr_scheduled
                temp_data = p.data - update_with_lr
                p.data = (torch.clamp(temp_data, min = 1e-4))

                state['step'] += 1
                p.data = p.data.to(dtype)
                p.grad = None

        return loss


class LSQSGD(Optimizer):
    """LSQ Gradient descent (with momentum) optimizer"""

    def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
                 b1_lsqsgd=0, b2_lsqsgd=0.999, e=1e-6, weight_decay=0.01,
                 max_grad_norm=1.0):
        if lr is not required and lr < 0.0:
            raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
        if schedule not in SCHEDULES:
            raise ValueError("Invalid schedule parameter: {}".format(schedule))
        if not 0.0 <= warmup < 1.0 and not warmup == -1:
            raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
        if not 0.0 <= b1_lsqsgd < 1.0:
            raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1_lsqsgd))
        if not 0.0 <= b2_lsqsgd < 1.0:
            raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2_lsqsgd))
        if not e >= 0.0:
            raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
        defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
                        b1=b1_lsqsgd, b2=b2_lsqsgd, e=e, weight_decay=weight_decay,
                        max_grad_norm=max_grad_norm)
        super(LSQSGD, self).__init__(params, defaults)

    def get_lr(self):
        lr = []
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                if len(state) == 0:
                    return [0]
                if group['t_total'] != -1:
                    schedule_fct = SCHEDULES[group['schedule']]
                    lr_scheduled = group['lr'] * schedule_fct(state['step'] / group['t_total'], group['warmup'])
                else:
                    lr_scheduled = group['lr']
                lr.append(lr_scheduled)
        return lr


    def step(self, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()
        for group in self.param_groups:
            for p in group['params']:
                if p.dtype == torch.float16:
                    p.data = p.data.float()
                # if p.numel() == 1:
                # print('Catch amax',p)
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['next_m'] = torch.zeros_like(p.data)
                    state['eff_lr'] = 0

                next_m = state['next_m']
                beta1 = group['b1']

                # Decay the first and second moment running average coefficient
                # In-place operations to update the averages at the same time
                next_m.mul_(beta1).add_(1 - beta1, grad)
                update = next_m

                # Just adding the square of the weights to the loss function is *not*
                # the correct way of using L2 regularization/weight decay with Adam,
                # since that will interact with the m and v parameters in strange ways.
                #
                # Instead we want to decay the weights in a manner that doesn't interact
                # with the m/v parameters. This is equivalent to adding the square
                # of the weights to the loss with plain (non-momentum) SGD.
                if group['weight_decay'] > 0.0:
                    update += group['weight_decay'] * p.data

                if group['t_total'] != -1:
                    schedule_fct = SCHEDULES[group['schedule']]
                    lr_scheduled = group['lr'] * schedule_fct(state['step'] / group['t_total'], group['warmup'])
                else:
                    lr_scheduled = group['lr']

                update_with_lr = lr_scheduled * update
                state['eff_lr'] = lr_scheduled
                p.data.add_(-update_with_lr)
                state['step'] += 1
                p.data = p.data.half()
                p.grad = None

        return loss
